Note
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RecoBundles80 using AFQ API¶
An example using the AFQ API to run recobundles with the 80 bundle atlas.
import os.path as op
import plotly
from AFQ import api
import AFQ.data as afd
Get some example data¶
Retrieves Stanford HARDI dataset.
afd.organize_stanford_data(clear_previous_afq=True)
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Set tractography parameters (optional)¶
We make this tracking_params which we will pass to the AFQ object which specifies that we want 50,000 seeds randomly distributed in the white matter.
We only do this to make this example faster and consume less space.
tracking_params = dict(n_seeds=50000,
random_seeds=True,
rng_seed=42)
Initialize an AFQ object:¶
We specify seg_algo as reco80 in segmentation_params. This tells the AFQ object to perform RecoBundles using the 80 bundles atlas in the segmentation step.
myafq = api.AFQ(bids_path=op.join(afd.afq_home,
'stanford_hardi'),
dmriprep='vistasoft',
segmentation_params={"seg_algo": "reco80"},
tracking_params=tracking_params)
Out:
Downloading https://templateflow.s3.amazonaws.com/tpl-MNI152NLin2009cAsym/tpl-MNI152NLin2009cAsym_res-01_T1w.nii.gz
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99%|#########9| 13.3k/13.4k [00:00<00:00, 46.6kB/s]
13.4kB [00:00, 44.1kB/s]
Downloading https://templateflow.s3.amazonaws.com/tpl-MNI152NLin2009cAsym/tpl-MNI152NLin2009cAsym_res-01_desc-brain_mask.nii.gz
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Visualizing bundles and tract profiles:¶
This would run the script and visualize the bundles using the plotly interactive visualization, which should automatically open in a new browser window.
bundle_html = myafq.viz_bundles(export=True, n_points=50)
plotly.io.show(bundle_html[0])